Models representing data relationships and patterns, such as functions, algorithms, systems, and the like, may accept input (sometimes referred to as an input vector), and produce output (sometimes referred to as an output vector) that corresponds to the input in some way. For example, a model may be implemented as a linear machine learning model such as a linear regression model. A machine learning algorithm such as a linear regression or a linear classification algorithm can be used to learn a machine learning model. The parameters of a machine learning model can be learned in a process referred to as training. For example, the parameters of a linear machine learning model can be learned using training data such as historical data that includes input data and the correct or preferred output of the model for the corresponding input data. A linear machine learning model may be a suitable model when the training data includes large scale historical data. However, a linear machine learning model may require training data to be numeric or binary even though the historical data may include categorical variables representing categorical features that can be predictive of the output of the linear machine learning model.